Pandas Profiling Report

Dataset statistics

Number of variables	39
Number of observations	1000
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	304.8 KiB
Average record size in memory	312.1 B

Variable types

Numeric	14
Categorical	24
Boolean	1

Alerts

`policy_bind_date` has a high cardinality: 951 distinct values	High cardinality
`incident_date` has a high cardinality: 60 distinct values	High cardinality
`incident_location` has a high cardinality: 1000 distinct values	High cardinality
`months_as_customer` is highly correlated with `age`	High correlation
`age` is highly correlated with `months_as_customer`	High correlation
`total_claim_amount` is highly correlated with `injury_claim` and 2 other fields	High correlation
`injury_claim` is highly correlated with `total_claim_amount` and 2 other fields	High correlation
`property_claim` is highly correlated with `total_claim_amount` and 2 other fields	High correlation
`vehicle_claim` is highly correlated with `total_claim_amount` and 2 other fields	High correlation
`months_as_customer` is highly correlated with `age`	High correlation
`age` is highly correlated with `months_as_customer`	High correlation
`total_claim_amount` is highly correlated with `injury_claim` and 2 other fields	High correlation
`injury_claim` is highly correlated with `total_claim_amount` and 2 other fields	High correlation
`property_claim` is highly correlated with `total_claim_amount` and 2 other fields	High correlation
`vehicle_claim` is highly correlated with `total_claim_amount` and 2 other fields	High correlation
`months_as_customer` is highly correlated with `age`	High correlation
`age` is highly correlated with `months_as_customer`	High correlation
`total_claim_amount` is highly correlated with `injury_claim` and 2 other fields	High correlation
`injury_claim` is highly correlated with `total_claim_amount` and 1 other fields	High correlation
`property_claim` is highly correlated with `total_claim_amount` and 1 other fields	High correlation
`vehicle_claim` is highly correlated with `total_claim_amount` and 2 other fields	High correlation
`incident_severity` is highly correlated with `fraud_reported`	High correlation
`collision_type` is highly correlated with `incident_type`	High correlation
`number_of_vehicles_involved` is highly correlated with `incident_type`	High correlation
`incident_type` is highly correlated with `collision_type` and 1 other fields	High correlation
`auto_make` is highly correlated with `auto_model`	High correlation
`fraud_reported` is highly correlated with `incident_severity`	High correlation
`auto_model` is highly correlated with `auto_make`	High correlation
`months_as_customer` is highly correlated with `age`	High correlation
`age` is highly correlated with `months_as_customer`	High correlation
`incident_type` is highly correlated with `collision_type` and 7 other fields	High correlation
`collision_type` is highly correlated with `incident_type` and 7 other fields	High correlation
`incident_severity` is highly correlated with `incident_type` and 6 other fields	High correlation
`authorities_contacted` is highly correlated with `incident_type` and 5 other fields	High correlation
`number_of_vehicles_involved` is highly correlated with `incident_type` and 1 other fields	High correlation
`total_claim_amount` is highly correlated with `incident_type` and 6 other fields	High correlation
`injury_claim` is highly correlated with `incident_type` and 6 other fields	High correlation
`property_claim` is highly correlated with `incident_type` and 6 other fields	High correlation
`vehicle_claim` is highly correlated with `incident_type` and 6 other fields	High correlation
`auto_make` is highly correlated with `auto_model`	High correlation
`auto_model` is highly correlated with `auto_make`	High correlation
`fraud_reported` is highly correlated with `incident_severity`	High correlation
`policy_bind_date` is uniformly distributed	Uniform
`incident_location` is uniformly distributed	Uniform
`policy_number` has unique values	Unique
`incident_location` has unique values	Unique
`umbrella_limit` has 798 (79.8%) zeros	Zeros
`capital-gains` has 508 (50.8%) zeros	Zeros
`capital-loss` has 475 (47.5%) zeros	Zeros
`incident_hour_of_the_day` has 52 (5.2%) zeros	Zeros
`injury_claim` has 25 (2.5%) zeros	Zeros
`property_claim` has 19 (1.9%) zeros	Zeros

Reproduction

Analysis started	2022-02-20 17:39:31.995895
Analysis finished	2022-02-20 17:40:19.428214
Duration	47.43 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

months_as_customer
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	391
Distinct (%)	39.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	203.954

Minimum	0
Maximum	479
Zeros	1
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0
5-th percentile	28.9
Q1	115.75
median	199.5
Q3	276.25
95-th percentile	429.05
Maximum	479
Range	479
Interquartile range (IQR)	160.5

Descriptive statistics

Standard deviation	115.1131744
Coefficient of variation (CV)	0.5644075352
Kurtosis	-0.4854280674
Mean	203.954
Median Absolute Deviation (MAD)	80.5
Skewness	0.3621768478
Sum	203954
Variance	13251.04293
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
194	8	0.8%
128	7	0.7%
254	7	0.7%
140	7	0.7%
210	7	0.7%
230	7	0.7%
285	7	0.7%
101	7	0.7%
239	6	0.6%
126	6	0.6%
Other values (381)	931	93.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	0.1%
1	3	0.3%
2	2	0.2%
3	2	0.2%
4	3	0.3%
5	2	0.2%
6	1	0.1%
7	1	0.1%
8	3	0.3%
9	2	0.2%

Value	Count	Frequency (%)
479	2	0.2%
478	2	0.2%
476	1	0.1%
475	2	0.2%
473	1	0.1%
472	1	0.1%
468	1	0.1%
467	1	0.1%
465	1	0.1%
464	1	0.1%

age
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	46
Distinct (%)	4.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	38.948

Minimum	19
Maximum	64
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	19
5-th percentile	26
Q1	32
median	38
Q3	44
95-th percentile	57
Maximum	64
Range	45
Interquartile range (IQR)	12

Descriptive statistics

Standard deviation	9.140286694
Coefficient of variation (CV)	0.2346792311
Kurtosis	-0.260255015
Mean	38.948
Median Absolute Deviation (MAD)	6
Skewness	0.4789880471
Sum	38948
Variance	83.54484084
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=46)

Value	Count	Frequency (%)
43	49	4.9%
39	48	4.8%
41	45	4.5%
34	44	4.4%
38	42	4.2%
30	42	4.2%
31	42	4.2%
37	41	4.1%
33	39	3.9%
40	38	3.8%
Other values (36)	570	57.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
19	1	0.1%
20	1	0.1%
21	6	0.6%
22	1	0.1%
23	7	0.7%
24	10	1.0%
25	14	1.4%
26	26	2.6%
27	24	2.4%
28	30	3.0%

Value	Count	Frequency (%)
64	2	0.2%
63	2	0.2%
62	4	0.4%
61	10	1.0%
60	9	0.9%
59	5	0.5%
58	8	0.8%
57	16	1.6%
56	8	0.8%
55	14	1.4%

policy_number
Real number (ℝ_≥0)

UNIQUE

Distinct	1000
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	546238.648

Minimum	100804
Maximum	999435
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	100804
5-th percentile	143969.6
Q1	335980.25
median	533135
Q3	759099.75
95-th percentile	954279.1
Maximum	999435
Range	898631
Interquartile range (IQR)	423119.5

Descriptive statistics

Standard deviation	257063.0053
Coefficient of variation (CV)	0.4706056706
Kurtosis	-1.132637689
Mean	546238.648
Median Absolute Deviation (MAD)	210974
Skewness	0.03899064218
Sum	546238648
Variance	6.608138868 × 10¹⁰
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
521585	1	0.1%
687755	1	0.1%
674485	1	0.1%
223404	1	0.1%
991480	1	0.1%
804219	1	0.1%
483088	1	0.1%
100804	1	0.1%
941807	1	0.1%
593466	1	0.1%
Other values (990)	990	99.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
100804	1	0.1%
101421	1	0.1%
104594	1	0.1%
106186	1	0.1%
106873	1	0.1%
107181	1	0.1%
108270	1	0.1%
108844	1	0.1%
109392	1	0.1%
110084	1	0.1%

Value	Count	Frequency (%)
999435	1	0.1%
998865	1	0.1%
998192	1	0.1%
996850	1	0.1%
996253	1	0.1%
994538	1	0.1%
993840	1	0.1%
992145	1	0.1%
991553	1	0.1%
991480	1	0.1%

policy_bind_date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	951
Distinct (%)	95.1%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

2006-01-01	3
1992-04-28	3
1992-08-05	3
1991-12-14	2
2004-08-09	2
Other values (946)	987

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	905 ?
Unique (%)	90.5%

Sample

1st row	2014-10-17
2nd row	2006-06-27
3rd row	2000-09-06
4th row	1990-05-25
5th row	2014-06-06

Common Values

Value	Count	Frequency (%)
2006-01-01	3	0.3%
1992-04-28	3	0.3%
1992-08-05	3	0.3%
1991-12-14	2	0.2%
2004-08-09	2	0.2%
2010-01-28	2	0.2%
1999-09-29	2	0.2%
2001-09-25	2	0.2%
2000-05-04	2	0.2%
1997-02-03	2	0.2%
Other values (941)	977	97.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2006-01-01	3	0.3%
1992-08-05	3	0.3%
1992-04-28	3	0.3%
1996-09-21	2	0.2%
2014-07-05	2	0.2%
1997-05-15	2	0.2%
1992-04-14	2	0.2%
2003-03-09	2	0.2%
1997-11-07	2	0.2%
1992-01-05	2	0.2%
Other values (941)	977	97.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

policy_state
Categorical

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

OH	352
IL	338
IN	310

Length

Max length	2
Median length	2
Mean length	2
Min length	2

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	OH
2nd row	IN
3rd row	OH
4th row	IL
5th row	IL

Common Values

Value	Count	Frequency (%)
OH	352	35.2%
IL	338	33.8%
IN	310	31.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
oh	352	35.2%
il	338	33.8%
in	310	31.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

policy_csl
Categorical

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

250/500	351
100/300	349
500/1000	300

Length

Max length	8
Median length	7
Mean length	7.3
Min length	7

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	250/500
2nd row	250/500
3rd row	100/300
4th row	250/500
5th row	500/1000

Common Values

Value	Count	Frequency (%)
250/500	351	35.1%
100/300	349	34.9%
500/1000	300	30.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
250/500	351	35.1%
100/300	349	34.9%
500/1000	300	30.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

policy_deductable
Categorical

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

1000	351
500	342
2000	307

Length

Max length	4
Median length	4
Mean length	3.658
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1000
2nd row	2000
3rd row	2000
4th row	2000
5th row	1000

Common Values

Value	Count	Frequency (%)
1000	351	35.1%
500	342	34.2%
2000	307	30.7%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1000	351	35.1%
500	342	34.2%
2000	307	30.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

policy_annual_premium
Real number (ℝ_≥0)

Distinct	991
Distinct (%)	99.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1256.40615

Minimum	433.33
Maximum	2047.59
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	433.33
5-th percentile	855.112
Q1	1089.6075
median	1257.2
Q3	1415.695
95-th percentile	1653.4435
Maximum	2047.59
Range	1614.26
Interquartile range (IQR)	326.0875

Descriptive statistics

Standard deviation	244.167395
Coefficient of variation (CV)	0.1943379495
Kurtosis	0.07388944021
Mean	1256.40615
Median Absolute Deviation (MAD)	164.26
Skewness	0.004401994527
Sum	1256406.15
Variance	59617.71676
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1558.29	2	0.2%
1215.36	2	0.2%
1362.87	2	0.2%
1073.83	2	0.2%
1389.13	2	0.2%
1074.07	2	0.2%
1374.22	2	0.2%
1524.45	2	0.2%
1281.25	2	0.2%
1230.69	1	0.1%
Other values (981)	981	98.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
433.33	1	0.1%
484.67	1	0.1%
538.17	1	0.1%
566.11	1	0.1%
617.11	1	0.1%
625.08	1	0.1%
653.66	1	0.1%
664.86	1	0.1%
671.01	1	0.1%
671.92	1	0.1%

Value	Count	Frequency (%)
2047.59	1	0.1%
1969.63	1	0.1%
1935.85	1	0.1%
1927.87	1	0.1%
1922.84	1	0.1%
1896.91	1	0.1%
1878.44	1	0.1%
1865.83	1	0.1%
1863.04	1	0.1%
1861.43	1	0.1%

umbrella_limit
Real number (ℝ)

ZEROS

Distinct	11
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1101000

Minimum	-1000000
Maximum	10000000
Zeros	798
Zeros (%)	79.8%
Negative	1
Negative (%)	0.1%
Memory size	7.9 KiB

Quantile statistics

Minimum	-1000000
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	6000000
Maximum	10000000
Range	11000000
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	2297406.598
Coefficient of variation (CV)	2.086654494
Kurtosis	1.79207731
Mean	1101000
Median Absolute Deviation (MAD)	0
Skewness	1.806712199
Sum	1101000000
Variance	5.278077077 × 10¹²
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
0	798	79.8%
6000000	57	5.7%
5000000	46	4.6%
4000000	39	3.9%
7000000	29	2.9%
3000000	12	1.2%
8000000	8	0.8%
9000000	5	0.5%
2000000	3	0.3%
10000000	2	0.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-1000000	1	0.1%
0	798	79.8%
2000000	3	0.3%
3000000	12	1.2%
4000000	39	3.9%
5000000	46	4.6%
6000000	57	5.7%
7000000	29	2.9%
8000000	8	0.8%
9000000	5	0.5%

Value	Count	Frequency (%)
10000000	2	0.2%
9000000	5	0.5%
8000000	8	0.8%
7000000	29	2.9%
6000000	57	5.7%
5000000	46	4.6%
4000000	39	3.9%
3000000	12	1.2%
2000000	3	0.3%
0	798	79.8%

insured_zip
Real number (ℝ_≥0)

Distinct	995
Distinct (%)	99.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	501214.488

Minimum	430104
Maximum	620962
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	430104
5-th percentile	433273.75
Q1	448404.5
median	466445.5
Q3	603251
95-th percentile	617463.35
Maximum	620962
Range	190858
Interquartile range (IQR)	154846.5

Descriptive statistics

Standard deviation	71701.61094
Coefficient of variation (CV)	0.1430557429
Kurtosis	-1.190711054
Mean	501214.488
Median Absolute Deviation (MAD)	21841
Skewness	0.8165539259
Sum	501214488
Variance	5141121012
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
477695	2	0.2%
469429	2	0.2%
446895	2	0.2%
431202	2	0.2%
456602	2	0.2%
466132	1	0.1%
452218	1	0.1%
608982	1	0.1%
459630	1	0.1%
453193	1	0.1%
Other values (985)	985	98.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
430104	1	0.1%
430141	1	0.1%
430232	1	0.1%
430380	1	0.1%
430567	1	0.1%
430621	1	0.1%
430632	1	0.1%
430665	1	0.1%
430714	1	0.1%
430832	1	0.1%

Value	Count	Frequency (%)
620962	1	0.1%
620869	1	0.1%
620819	1	0.1%
620757	1	0.1%
620737	1	0.1%
620507	1	0.1%
620493	1	0.1%
620473	1	0.1%
620358	1	0.1%
620207	1	0.1%

insured_sex
Categorical

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

FEMALE	537
MALE	463

Length

Max length	6
Median length	6
Mean length	5.074
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	MALE
2nd row	MALE
3rd row	FEMALE
4th row	FEMALE
5th row	MALE

Common Values

Value	Count	Frequency (%)
FEMALE	537	53.7%
MALE	463	46.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
female	537	53.7%
male	463	46.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

insured_education_level
Categorical

Distinct	7
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

JD	161
High School	160
Associate	145
MD	144
Masters	143
Other values (2)	247

Length

Max length	11
Median length	7
Mean length	5.905
Min length	2

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	MD
2nd row	MD
3rd row	PhD
4th row	PhD
5th row	Associate

Common Values

Value	Count	Frequency (%)
JD	161	16.1%
High School	160	16.0%
Associate	145	14.5%
MD	144	14.4%
Masters	143	14.3%
PhD	125	12.5%
College	122	12.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
jd	161	13.9%
high	160	13.8%
school	160	13.8%
associate	145	12.5%
md	144	12.4%
masters	143	12.3%
phd	125	10.8%
college	122	10.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

insured_occupation
Categorical

Distinct	14
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

machine-op-inspct	93
prof-specialty	85
tech-support	78
sales	76
exec-managerial	76
Other values (9)	592

Length

Max length	17
Median length	14
Mean length	13.521
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	craft-repair
2nd row	machine-op-inspct
3rd row	sales
4th row	armed-forces
5th row	sales

Common Values

Value	Count	Frequency (%)
machine-op-inspct	93	9.3%
prof-specialty	85	8.5%
tech-support	78	7.8%
sales	76	7.6%
exec-managerial	76	7.6%
craft-repair	74	7.4%
transport-moving	72	7.2%
other-service	71	7.1%
priv-house-serv	71	7.1%
armed-forces	69	6.9%
Other values (4)	235	23.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
machine-op-inspct	93	9.3%
prof-specialty	85	8.5%
tech-support	78	7.8%
sales	76	7.6%
exec-managerial	76	7.6%
craft-repair	74	7.4%
transport-moving	72	7.2%
other-service	71	7.1%
priv-house-serv	71	7.1%
armed-forces	69	6.9%
Other values (4)	235	23.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

insured_hobbies
Categorical

Distinct	20
Distinct (%)	2.0%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

reading	64
exercise	57
paintball	57
bungie-jumping	56
movies	55
Other values (15)	711

Length

Max length	14
Median length	8
Mean length	8.113
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	sleeping
2nd row	reading
3rd row	board-games
4th row	board-games
5th row	board-games

Common Values

Value	Count	Frequency (%)
reading	64	6.4%
exercise	57	5.7%
paintball	57	5.7%
bungie-jumping	56	5.6%
movies	55	5.5%
golf	55	5.5%
camping	55	5.5%
kayaking	54	5.4%
yachting	53	5.3%
hiking	52	5.2%
Other values (10)	442	44.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
reading	64	6.4%
exercise	57	5.7%
paintball	57	5.7%
bungie-jumping	56	5.6%
movies	55	5.5%
golf	55	5.5%
camping	55	5.5%
kayaking	54	5.4%
yachting	53	5.3%
hiking	52	5.2%
Other values (10)	442	44.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

insured_relationship
Categorical

Distinct	6
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

own-child	183
other-relative	177
not-in-family	174
husband	170
wife	155

Length

Max length	14
Median length	9
Mean length	9.466
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	husband
2nd row	other-relative
3rd row	own-child
4th row	unmarried
5th row	unmarried

Common Values

Value	Count	Frequency (%)
own-child	183	18.3%
other-relative	177	17.7%
not-in-family	174	17.4%
husband	170	17.0%
wife	155	15.5%
unmarried	141	14.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
own-child	183	18.3%
other-relative	177	17.7%
not-in-family	174	17.4%
husband	170	17.0%
wife	155	15.5%
unmarried	141	14.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

capital-gains
Real number (ℝ_≥0)

ZEROS

Distinct	338
Distinct (%)	33.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	25126.1

Minimum	0
Maximum	100500
Zeros	508
Zeros (%)	50.8%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	51025
95-th percentile	70615
Maximum	100500
Range	100500
Interquartile range (IQR)	51025

Descriptive statistics

Standard deviation	27872.18771
Coefficient of variation (CV)	1.109292238
Kurtosis	-1.276703511
Mean	25126.1
Median Absolute Deviation (MAD)	0
Skewness	0.4788502296
Sum	25126100
Variance	776858847.6
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	508	50.8%
46300	5	0.5%
51500	4	0.4%
68500	4	0.4%
55600	3	0.3%
49700	3	0.3%
51700	3	0.3%
56700	3	0.3%
47600	3	0.3%
44000	3	0.3%
Other values (328)	461	46.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	508	50.8%
800	1	0.1%
10000	1	0.1%
11000	1	0.1%
12100	1	0.1%
12800	1	0.1%
13100	1	0.1%
14100	1	0.1%
16100	1	0.1%
17300	1	0.1%

Value	Count	Frequency (%)
100500	1	0.1%
98800	1	0.1%
94800	1	0.1%
91900	1	0.1%
90700	1	0.1%
88800	1	0.1%
88400	1	0.1%
87800	1	0.1%
84900	1	0.1%
83900	1	0.1%

capital-loss
Real number (ℝ)

ZEROS

Distinct	354
Distinct (%)	35.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	-26793.7

Minimum	-111100
Maximum	0
Zeros	475
Zeros (%)	47.5%
Negative	525
Negative (%)	52.5%
Memory size	7.9 KiB

Quantile statistics

Minimum	-111100
5-th percentile	-72305
Q1	-51500
median	-23250
Q3	0
95-th percentile	0
Maximum	0
Range	111100
Interquartile range (IQR)	51500

Descriptive statistics

Standard deviation	28104.09669
Coefficient of variation (CV)	-1.048906896
Kurtosis	-1.3138745
Mean	-26793.7
Median Absolute Deviation (MAD)	23250
Skewness	-0.391471943
Sum	-26793700
Variance	789840250.6
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	475	47.5%
-31700	5	0.5%
-53700	5	0.5%
-50300	5	0.5%
-45300	4	0.4%
-51000	4	0.4%
-32800	4	0.4%
-53800	4	0.4%
-49200	4	0.4%
-31400	4	0.4%
Other values (344)	486	48.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-111100	1	0.1%
-93600	1	0.1%
-91400	1	0.1%
-91200	1	0.1%
-90600	1	0.1%
-90200	1	0.1%
-90100	1	0.1%
-89400	1	0.1%
-88300	1	0.1%
-87300	1	0.1%

Value	Count	Frequency (%)
0	475	47.5%
-5700	1	0.1%
-6300	1	0.1%
-8500	1	0.1%
-10600	1	0.1%
-12100	1	0.1%
-13200	1	0.1%
-13800	1	0.1%
-15600	1	0.1%
-15700	2	0.2%

incident_date
Categorical

HIGH CARDINALITY

Distinct	60
Distinct (%)	6.0%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

2015-02-02	28
2015-02-17	26
2015-01-07	25
2015-01-10	24
2015-02-04	24
Other values (55)	873

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2015-01-25
2nd row	2015-01-21
3rd row	2015-02-22
4th row	2015-01-10
5th row	2015-02-17

Common Values

Value	Count	Frequency (%)
2015-02-02	28	2.8%
2015-02-17	26	2.6%
2015-01-07	25	2.5%
2015-01-10	24	2.4%
2015-02-04	24	2.4%
2015-01-24	24	2.4%
2015-01-19	23	2.3%
2015-01-08	22	2.2%
2015-01-13	21	2.1%
2015-01-30	21	2.1%
Other values (50)	762	76.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2015-02-02	28	2.8%
2015-02-17	26	2.6%
2015-01-07	25	2.5%
2015-02-04	24	2.4%
2015-01-24	24	2.4%
2015-01-10	24	2.4%
2015-01-19	23	2.3%
2015-01-08	22	2.2%
2015-01-13	21	2.1%
2015-01-30	21	2.1%
Other values (50)	762	76.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

incident_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	4
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Multi-vehicle Collision	419
Single Vehicle Collision	403
Vehicle Theft	94
Parked Car	84

Length

Max length	24
Median length	23
Mean length	21.371
Min length	10

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Single Vehicle Collision
2nd row	Vehicle Theft
3rd row	Multi-vehicle Collision
4th row	Single Vehicle Collision
5th row	Vehicle Theft

Common Values

Value	Count	Frequency (%)
Multi-vehicle Collision	419	41.9%
Single Vehicle Collision	403	40.3%
Vehicle Theft	94	9.4%
Parked Car	84	8.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
collision	822	34.2%
vehicle	497	20.7%
multi-vehicle	419	17.4%
single	403	16.8%
theft	94	3.9%
parked	84	3.5%
car	84	3.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

collision_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	4
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Rear Collision	292
Side Collision	276
Front Collision	254
?	178

Length

Max length	15
Median length	14
Mean length	11.94
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Side Collision
2nd row	?
3rd row	Rear Collision
4th row	Front Collision
5th row	?

Common Values

Value	Count	Frequency (%)
Rear Collision	292	29.2%
Side Collision	276	27.6%
Front Collision	254	25.4%
?	178	17.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
collision	822	45.1%
rear	292	16.0%
side	276	15.1%
front	254	13.9%
	178	9.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

incident_severity
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	4
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Minor Damage	354
Total Loss	280
Major Damage	276
Trivial Damage	90

Length

Max length	14
Median length	12
Mean length	11.62
Min length	10

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Major Damage
2nd row	Minor Damage
3rd row	Minor Damage
4th row	Major Damage
5th row	Minor Damage

Common Values

Value	Count	Frequency (%)
Minor Damage	354	35.4%
Total Loss	280	28.0%
Major Damage	276	27.6%
Trivial Damage	90	9.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
damage	720	36.0%
minor	354	17.7%
total	280	14.0%
loss	280	14.0%
major	276	13.8%
trivial	90	4.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

authorities_contacted
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Police	292
Fire	223
Other	198
Ambulance	196
None	91

Length

Max length	9
Median length	5
Mean length	5.762
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Police
2nd row	Police
3rd row	Police
4th row	Police
5th row	None

Common Values

Value	Count	Frequency (%)
Police	292	29.2%
Fire	223	22.3%
Other	198	19.8%
Ambulance	196	19.6%
None	91	9.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
police	292	29.2%
fire	223	22.3%
other	198	19.8%
ambulance	196	19.6%
none	91	9.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

incident_state
Categorical

Distinct	7
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

NY	262
SC	248
WV	217
VA	110
NC	110
Other values (2)	53

Length

Max length	2
Median length	2
Mean length	2
Min length	2

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	SC
2nd row	VA
3rd row	NY
4th row	OH
5th row	NY

Common Values

Value	Count	Frequency (%)
NY	262	26.2%
SC	248	24.8%
WV	217	21.7%
VA	110	11.0%
NC	110	11.0%
PA	30	3.0%
OH	23	2.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
ny	262	26.2%
sc	248	24.8%
wv	217	21.7%
va	110	11.0%
nc	110	11.0%
pa	30	3.0%
oh	23	2.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

incident_city
Categorical

Distinct	7
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Springfield	157
Arlington	152
Columbus	149
Northbend	145
Hillsdale	141
Other values (2)	256

Length

Max length	11
Median length	9
Mean length	9.287
Min length	8

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Columbus
2nd row	Riverwood
3rd row	Columbus
4th row	Arlington
5th row	Arlington

Common Values

Value	Count	Frequency (%)
Springfield	157	15.7%
Arlington	152	15.2%
Columbus	149	14.9%
Northbend	145	14.5%
Hillsdale	141	14.1%
Riverwood	134	13.4%
Northbrook	122	12.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
springfield	157	15.7%
arlington	152	15.2%
columbus	149	14.9%
northbend	145	14.5%
hillsdale	141	14.1%
riverwood	134	13.4%
northbrook	122	12.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

incident_location
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	1000
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

9935 4th Drive	1
4214 MLK Ridge	1
8548 Cherokee Ridge	1
2352 MLK Drive	1
9734 2nd Ridge	1
Other values (995)	995

Length

Max length	23
Median length	14
Mean length	14.749
Min length	11

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1000 ?
Unique (%)	100.0%

Sample

1st row	9935 4th Drive
2nd row	6608 MLK Hwy
3rd row	7121 Francis Lane
4th row	6956 Maple Drive
5th row	3041 3rd Ave

Common Values

Value	Count	Frequency (%)
9935 4th Drive	1	0.1%
4214 MLK Ridge	1	0.1%
8548 Cherokee Ridge	1	0.1%
2352 MLK Drive	1	0.1%
9734 2nd Ridge	1	0.1%
3122 Apache Drive	1	0.1%
9816 Britain St	1	0.1%
8214 Flute St	1	0.1%
6259 Lincoln Hwy	1	0.1%
4492 Andromedia Ave	1	0.1%
Other values (990)	990	99.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
drive	173	5.8%
lane	171	5.7%
ridge	171	5.7%
st	171	5.7%
ave	161	5.4%
hwy	153	5.1%
4th	57	1.9%
5th	52	1.7%
texas	47	1.6%
francis	45	1.5%
Other values (961)	1799	60.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

incident_hour_of_the_day
Real number (ℝ_≥0)

ZEROS

Distinct	24
Distinct (%)	2.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	11.644

Minimum	0
Maximum	23
Zeros	52
Zeros (%)	5.2%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	6
median	12
Q3	17
95-th percentile	23
Maximum	23
Range	23
Interquartile range (IQR)	11

Descriptive statistics

Standard deviation	6.951372928
Coefficient of variation (CV)	0.5969918351
Kurtosis	-1.192940152
Mean	11.644
Median Absolute Deviation (MAD)	6
Skewness	-0.03558446644
Sum	11644
Variance	48.32158559
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=24)

Value	Count	Frequency (%)
17	54	5.4%
3	53	5.3%
0	52	5.2%
23	51	5.1%
16	49	4.9%
13	46	4.6%
10	46	4.6%
4	46	4.6%
6	44	4.4%
9	43	4.3%
Other values (14)	516	51.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	52	5.2%
1	29	2.9%
2	31	3.1%
3	53	5.3%
4	46	4.6%
5	33	3.3%
6	44	4.4%
7	40	4.0%
8	36	3.6%
9	43	4.3%

Value	Count	Frequency (%)
23	51	5.1%
22	38	3.8%
21	42	4.2%
20	34	3.4%
19	40	4.0%
18	41	4.1%
17	54	5.4%
16	49	4.9%
15	39	3.9%
14	43	4.3%

number_of_vehicles_involved
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	4
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

1	581
3	358
4	31
2	30

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	3
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	581	58.1%
3	358	35.8%
4	31	3.1%
2	30	3.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	581	58.1%
3	358	35.8%
4	31	3.1%
2	30	3.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

property_damage
Categorical

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

?	360
NO	338
YES	302

Length

Max length	3
Median length	2
Mean length	1.942
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	YES
2nd row	?
3rd row	NO
4th row	?
5th row	NO

Common Values

Value	Count	Frequency (%)
?	360	36.0%
NO	338	33.8%
YES	302	30.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
	360	36.0%
no	338	33.8%
yes	302	30.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

bodily_injuries
Categorical

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

0	340
2	332
1	328

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	2
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
0	340	34.0%
2	332	33.2%
1	328	32.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	340	34.0%
2	332	33.2%
1	328	32.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

witnesses
Categorical

Distinct	4
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

1	258
2	250
0	249
3	243

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2
2nd row	0
3rd row	3
4th row	2
5th row	1

Common Values

Value	Count	Frequency (%)
1	258	25.8%
2	250	25.0%
0	249	24.9%
3	243	24.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	258	25.8%
2	250	25.0%
0	249	24.9%
3	243	24.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

police_report_available
Categorical

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

?	343
NO	343
YES	314

Length

Max length	3
Median length	2
Mean length	1.971
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	YES
2nd row	?
3rd row	NO
4th row	NO
5th row	NO

Common Values

Value	Count	Frequency (%)
?	343	34.3%
NO	343	34.3%
YES	314	31.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
	343	34.3%
no	343	34.3%
yes	314	31.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

total_claim_amount
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	763
Distinct (%)	76.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	52761.94

Minimum	100
Maximum	114920
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	100
5-th percentile	4320
Q1	41812.5
median	58055
Q3	70592.5
95-th percentile	88413
Maximum	114920
Range	114820
Interquartile range (IQR)	28780

Descriptive statistics

Standard deviation	26401.53319
Coefficient of variation (CV)	0.5003897353
Kurtosis	-0.4540814267
Mean	52761.94
Median Absolute Deviation (MAD)	13855
Skewness	-0.5945819885
Sum	52761940
Variance	697040954.8
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
59400	5	0.5%
2640	4	0.4%
70400	4	0.4%
4320	4	0.4%
44200	4	0.4%
75400	4	0.4%
60600	4	0.4%
3190	4	0.4%
58500	4	0.4%
70290	4	0.4%
Other values (753)	959	95.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
100	1	0.1%
1920	1	0.1%
2160	1	0.1%
2250	1	0.1%
2400	1	0.1%
2520	1	0.1%
2640	4	0.4%
2700	2	0.2%
2800	1	0.1%
2860	1	0.1%

Value	Count	Frequency (%)
114920	1	0.1%
112320	1	0.1%
108480	1	0.1%
108030	1	0.1%
107900	1	0.1%
105820	1	0.1%
105040	1	0.1%
104610	1	0.1%
103560	1	0.1%
101860	1	0.1%

injury_claim
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	638
Distinct (%)	63.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7433.42

Minimum	0
Maximum	21450
Zeros	25
Zeros (%)	2.5%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0
5-th percentile	450
Q1	4295
median	6775
Q3	11305
95-th percentile	15662
Maximum	21450
Range	21450
Interquartile range (IQR)	7010

Descriptive statistics

Standard deviation	4880.951853
Coefficient of variation (CV)	0.6566226385
Kurtosis	-0.7630870611
Mean	7433.42
Median Absolute Deviation (MAD)	3705
Skewness	0.2648108785
Sum	7433420
Variance	23823690.99
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	25	2.5%
640	7	0.7%
480	7	0.7%
660	5	0.5%
580	5	0.5%
13520	5	0.5%
1180	5	0.5%
860	5	0.5%
6340	5	0.5%
780	5	0.5%
Other values (628)	926	92.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	25	2.5%
10	1	0.1%
220	1	0.1%
250	1	0.1%
280	2	0.2%
290	1	0.1%
300	3	0.3%
330	2	0.2%
350	1	0.1%
360	1	0.1%

Value	Count	Frequency (%)
21450	1	0.1%
21330	1	0.1%
20700	1	0.1%
19020	1	0.1%
18520	1	0.1%
18220	1	0.1%
18180	1	0.1%
18080	1	0.1%
18000	1	0.1%
17880	1	0.1%

property_claim
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	626
Distinct (%)	62.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7399.57

Minimum	0
Maximum	23670
Zeros	19
Zeros (%)	1.9%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0
5-th percentile	450
Q1	4445
median	6750
Q3	10885
95-th percentile	15540
Maximum	23670
Range	23670
Interquartile range (IQR)	6440

Descriptive statistics

Standard deviation	4824.726179
Coefficient of variation (CV)	0.6520279122
Kurtosis	-0.3763863117
Mean	7399.57
Median Absolute Deviation (MAD)	3290
Skewness	0.3781687764
Sum	7399570
Variance	23277982.7
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	19	1.9%
860	6	0.6%
480	5	0.5%
660	5	0.5%
10000	5	0.5%
640	5	0.5%
650	5	0.5%
11080	5	0.5%
840	4	0.4%
5310	4	0.4%
Other values (616)	937	93.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	19	1.9%
20	1	0.1%
240	1	0.1%
250	1	0.1%
260	1	0.1%
280	3	0.3%
290	2	0.2%
300	3	0.3%
320	3	0.3%
330	1	0.1%

Value	Count	Frequency (%)
23670	1	0.1%
21810	1	0.1%
21630	1	0.1%
21580	1	0.1%
21240	1	0.1%
20550	1	0.1%
20310	1	0.1%
20280	1	0.1%
19950	1	0.1%
19650	1	0.1%

vehicle_claim
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	726
Distinct (%)	72.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	37928.95

Minimum	70
Maximum	79560
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	70
5-th percentile	3273.5
Q1	30292.5
median	42100
Q3	50822.5
95-th percentile	63094.5
Maximum	79560
Range	79490
Interquartile range (IQR)	20530

Descriptive statistics

Standard deviation	18886.25289
Coefficient of variation (CV)	0.4979376675
Kurtosis	-0.4465729231
Mean	37928.95
Median Absolute Deviation (MAD)	9840
Skewness	-0.6210979312
Sum	37928950
Variance	356690548.3
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5040	7	0.7%
3360	6	0.6%
52080	5	0.5%
4720	5	0.5%
3600	5	0.5%
44800	5	0.5%
33600	5	0.5%
42720	4	0.4%
41580	4	0.4%
35000	4	0.4%
Other values (716)	950	95.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
70	1	0.1%
1440	2	0.2%
1680	2	0.2%
1750	1	0.1%
1760	1	0.1%
1800	1	0.1%
1960	2	0.2%
1980	1	0.1%
2030	1	0.1%
2080	1	0.1%

Value	Count	Frequency (%)
79560	1	0.1%
77760	1	0.1%
77670	2	0.2%
76400	1	0.1%
76000	1	0.1%
75600	1	0.1%
75530	1	0.1%
74790	1	0.1%
73620	1	0.1%
73260	1	0.1%

auto_make
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	14
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Saab	80
Dodge	80
Suburu	80
Nissan	78
Chevrolet	76
Other values (9)	606

Length

Max length	10
Median length	6
Mean length	5.703
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Saab
2nd row	Mercedes
3rd row	Dodge
4th row	Chevrolet
5th row	Accura

Common Values

Value	Count	Frequency (%)
Saab	80	8.0%
Dodge	80	8.0%
Suburu	80	8.0%
Nissan	78	7.8%
Chevrolet	76	7.6%
Ford	72	7.2%
BMW	72	7.2%
Toyota	70	7.0%
Audi	69	6.9%
Accura	68	6.8%
Other values (4)	255	25.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
saab	80	8.0%
dodge	80	8.0%
suburu	80	8.0%
nissan	78	7.8%
chevrolet	76	7.6%
ford	72	7.2%
bmw	72	7.2%
toyota	70	7.0%
audi	69	6.9%
accura	68	6.8%
Other values (4)	255	25.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

auto_model
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	39
Distinct (%)	3.9%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

RAM	43
Wrangler	42
A3	37
Neon	37
MDX	36
Other values (34)	805

Length

Max length	14
Median length	5
Mean length	5.178
Min length	2

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	92x
2nd row	E400
3rd row	RAM
4th row	Tahoe
5th row	RSX

Common Values

Value	Count	Frequency (%)
RAM	43	4.3%
Wrangler	42	4.2%
A3	37	3.7%
Neon	37	3.7%
MDX	36	3.6%
Jetta	35	3.5%
Passat	33	3.3%
A5	32	3.2%
Legacy	32	3.2%
Pathfinder	31	3.1%
Other values (29)	642	64.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
ram	43	4.1%
wrangler	42	4.0%
a3	37	3.5%
neon	37	3.5%
mdx	36	3.5%
jetta	35	3.4%
passat	33	3.2%
a5	32	3.1%
legacy	32	3.1%
pathfinder	31	3.0%
Other values (31)	685	65.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

auto_year
Real number (ℝ_≥0)

Distinct	21
Distinct (%)	2.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2005.103

Minimum	1995
Maximum	2015
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	1995
5-th percentile	1995
Q1	2000
median	2005
Q3	2010
95-th percentile	2014
Maximum	2015
Range	20
Interquartile range (IQR)	10

Descriptive statistics

Standard deviation	6.015860835
Coefficient of variation (CV)	0.003000275215
Kurtosis	-1.171867756
Mean	2005.103
Median Absolute Deviation (MAD)	5
Skewness	-0.04828880711
Sum	2005103
Variance	36.19058158
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=21)

Value	Count	Frequency (%)
1995	56	5.6%
1999	55	5.5%
2005	54	5.4%
2006	53	5.3%
2011	53	5.3%
2007	52	5.2%
2003	51	5.1%
2009	50	5.0%
2010	50	5.0%
2013	49	4.9%
Other values (11)	477	47.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1995	56	5.6%
1996	37	3.7%
1997	46	4.6%
1998	40	4.0%
1999	55	5.5%
2000	42	4.2%
2001	42	4.2%
2002	49	4.9%
2003	51	5.1%
2004	39	3.9%

Value	Count	Frequency (%)
2015	47	4.7%
2014	44	4.4%
2013	49	4.9%
2012	46	4.6%
2011	53	5.3%
2010	50	5.0%
2009	50	5.0%
2008	45	4.5%
2007	52	5.2%
2006	53	5.3%

fraud_reported
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	1.1 KiB

False	753
True	247

Common Values
Chart

Value	Count	Frequency (%)
False	753	75.3%
True	247	24.7%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	months_as_customer	age	policy_number	policy_bind_date	policy_state	policy_csl	policy_deductable	policy_annual_premium	umbrella_limit	insured_zip	insured_sex	insured_education_level	insured_occupation	insured_hobbies	insured_relationship	capital-gains	capital-loss	incident_date	incident_type	collision_type	incident_severity	authorities_contacted	incident_state	incident_city	incident_location	incident_hour_of_the_day	number_of_vehicles_involved	property_damage	bodily_injuries	witnesses	police_report_available	total_claim_amount	injury_claim	property_claim	vehicle_claim	auto_make	auto_model	auto_year	fraud_reported
0	328	48	521585	2014-10-17	OH	250/500	1000	1406.91	0	466132	MALE	MD	craft-repair	sleeping	husband	53300	0	2015-01-25	Single Vehicle Collision	Side Collision	Major Damage	Police	SC	Columbus	9935 4th Drive	5	1	YES	1	2	YES	71610	6510	13020	52080	Saab	92x	2004	Y
1	228	42	342868	2006-06-27	IN	250/500	2000	1197.22	5000000	468176	MALE	MD	machine-op-inspct	reading	other-relative	0	0	2015-01-21	Vehicle Theft	?	Minor Damage	Police	VA	Riverwood	6608 MLK Hwy	8	1	?	0	0	?	5070	780	780	3510	Mercedes	E400	2007	Y
2	134	29	687698	2000-09-06	OH	100/300	2000	1413.14	5000000	430632	FEMALE	PhD	sales	board-games	own-child	35100	0	2015-02-22	Multi-vehicle Collision	Rear Collision	Minor Damage	Police	NY	Columbus	7121 Francis Lane	7	3	NO	2	3	NO	34650	7700	3850	23100	Dodge	RAM	2007	N
3	256	41	227811	1990-05-25	IL	250/500	2000	1415.74	6000000	608117	FEMALE	PhD	armed-forces	board-games	unmarried	48900	-62400	2015-01-10	Single Vehicle Collision	Front Collision	Major Damage	Police	OH	Arlington	6956 Maple Drive	5	1	?	1	2	NO	63400	6340	6340	50720	Chevrolet	Tahoe	2014	Y
4	228	44	367455	2014-06-06	IL	500/1000	1000	1583.91	6000000	610706	MALE	Associate	sales	board-games	unmarried	66000	-46000	2015-02-17	Vehicle Theft	?	Minor Damage	None	NY	Arlington	3041 3rd Ave	20	1	NO	0	1	NO	6500	1300	650	4550	Accura	RSX	2009	N
5	256	39	104594	2006-10-12	OH	250/500	1000	1351.10	0	478456	FEMALE	PhD	tech-support	bungie-jumping	unmarried	0	0	2015-01-02	Multi-vehicle Collision	Rear Collision	Major Damage	Fire	SC	Arlington	8973 Washington St	19	3	NO	0	2	NO	64100	6410	6410	51280	Saab	95	2003	Y
6	137	34	413978	2000-06-04	IN	250/500	1000	1333.35	0	441716	MALE	PhD	prof-specialty	board-games	husband	0	-77000	2015-01-13	Multi-vehicle Collision	Front Collision	Minor Damage	Police	NY	Springfield	5846 Weaver Drive	0	3	?	0	0	?	78650	21450	7150	50050	Nissan	Pathfinder	2012	N
7	165	37	429027	1990-02-03	IL	100/300	1000	1137.03	0	603195	MALE	Associate	tech-support	base-jumping	unmarried	0	0	2015-02-27	Multi-vehicle Collision	Front Collision	Total Loss	Police	VA	Columbus	3525 3rd Hwy	23	3	?	2	2	YES	51590	9380	9380	32830	Audi	A5	2015	N
8	27	33	485665	1997-02-05	IL	100/300	500	1442.99	0	601734	FEMALE	PhD	other-service	golf	own-child	0	0	2015-01-30	Single Vehicle Collision	Front Collision	Total Loss	Police	WV	Arlington	4872 Rock Ridge	21	1	NO	1	1	YES	27700	2770	2770	22160	Toyota	Camry	2012	N
9	212	42	636550	2011-07-25	IL	100/300	500	1315.68	0	600983	MALE	PhD	priv-house-serv	camping	wife	0	-39300	2015-01-05	Single Vehicle Collision	Rear Collision	Total Loss	Other	NC	Hillsdale	3066 Francis Ave	14	1	NO	2	1	?	42300	4700	4700	32900	Saab	92x	1996	N

Last rows

	months_as_customer	age	policy_number	policy_bind_date	policy_state	policy_csl	policy_deductable	policy_annual_premium	umbrella_limit	insured_zip	insured_sex	insured_education_level	insured_occupation	insured_hobbies	insured_relationship	capital-gains	capital-loss	incident_date	incident_type	collision_type	incident_severity	authorities_contacted	incident_state	incident_city	incident_location	incident_hour_of_the_day	number_of_vehicles_involved	property_damage	bodily_injuries	witnesses	police_report_available	total_claim_amount	injury_claim	property_claim	vehicle_claim	auto_make	auto_model	auto_year	fraud_reported
990	286	43	663190	1994-02-05	IL	100/300	500	1564.43	3000000	477644	FEMALE	MD	prof-specialty	movies	unmarried	77500	-32800	2015-01-31	Single Vehicle Collision	Rear Collision	Minor Damage	Fire	NY	Northbrook	4755 1st St	18	1	?	2	2	YES	34290	3810	3810	26670	Jeep	Grand Cherokee	2013	N
991	257	44	109392	2006-07-12	OH	100/300	1000	1280.88	0	433981	MALE	MD	other-service	basketball	other-relative	59400	-32200	2015-02-06	Single Vehicle Collision	Rear Collision	Total Loss	Other	WV	Riverwood	5312 Francis Ridge	21	1	NO	0	1	NO	46980	0	5220	41760	Accura	TL	2002	N
992	94	26	215278	2007-10-24	IN	100/300	500	722.66	0	433696	MALE	MD	exec-managerial	camping	husband	50300	0	2015-01-23	Multi-vehicle Collision	Front Collision	Major Damage	Fire	OH	Springfield	1705 Weaver St	6	3	YES	1	2	YES	36700	3670	7340	25690	Nissan	Pathfinder	2010	N
993	124	28	674570	2001-12-08	OH	250/500	1000	1235.14	0	443567	MALE	MD	exec-managerial	camping	husband	0	-32100	2015-02-17	Multi-vehicle Collision	Side Collision	Total Loss	Other	OH	Hillsdale	1643 Washington Hwy	20	3	?	0	1	?	60200	6020	6020	48160	Volkswagen	Passat	2012	N
994	141	30	681486	2007-03-24	IN	500/1000	1000	1347.04	0	430665	MALE	High School	sales	bungie-jumping	own-child	0	-82100	2015-01-22	Parked Car	?	Minor Damage	None	SC	Northbend	6516 Solo Drive	6	1	?	1	2	YES	6480	540	1080	4860	Honda	Civic	1996	N
995	3	38	941851	1991-07-16	OH	500/1000	1000	1310.80	0	431289	FEMALE	Masters	craft-repair	paintball	unmarried	0	0	2015-02-22	Single Vehicle Collision	Front Collision	Minor Damage	Fire	NC	Northbrook	6045 Andromedia St	20	1	YES	0	1	?	87200	17440	8720	61040	Honda	Accord	2006	N
996	285	41	186934	2014-01-05	IL	100/300	1000	1436.79	0	608177	FEMALE	PhD	prof-specialty	sleeping	wife	70900	0	2015-01-24	Single Vehicle Collision	Rear Collision	Major Damage	Fire	SC	Northbend	3092 Texas Drive	23	1	YES	2	3	?	108480	18080	18080	72320	Volkswagen	Passat	2015	N
997	130	34	918516	2003-02-17	OH	250/500	500	1383.49	3000000	442797	FEMALE	Masters	armed-forces	bungie-jumping	other-relative	35100	0	2015-01-23	Multi-vehicle Collision	Side Collision	Minor Damage	Police	NC	Arlington	7629 5th St	4	3	?	2	3	YES	67500	7500	7500	52500	Suburu	Impreza	1996	N
998	458	62	533940	2011-11-18	IL	500/1000	2000	1356.92	5000000	441714	MALE	Associate	handlers-cleaners	base-jumping	wife	0	0	2015-02-26	Single Vehicle Collision	Rear Collision	Major Damage	Other	NY	Arlington	6128 Elm Lane	2	1	?	0	1	YES	46980	5220	5220	36540	Audi	A5	1998	N
999	456	60	556080	1996-11-11	OH	250/500	1000	766.19	0	612260	FEMALE	Associate	sales	kayaking	husband	0	0	2015-02-26	Parked Car	?	Minor Damage	Police	WV	Columbus	1416 Cherokee Ridge	6	1	?	0	3	?	5060	460	920	3680	Mercedes	E400	2007	N

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values